Those detectors are usually defined as a classification task commonly solved using deep learning. Different features are used as parameters for the models. For example, in fake news identification, [1]} explored the combination of different models and features, including hand-designed features, word embeddings, ratings, number of comments and structural aspects of discussion trees.
In addition, another key element of the detectors is the datasets. For some threats (e.g. hate speech and fake news), few standard datasets target social media, but that is not the case for all the threats. In violent content detections, for example, there is not a standard dataset focused on SM in the best of our knowledge. In order to overcome this limitation, works such as [2]}, [3]} use a proxy dataset, such as Hockey Violence Dataset [4]}.
